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fix: rewrite two phrases flagged by GitHub automated spam scanner

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GitHub Support flagged "how do I" as matching a known support-scam
pattern. Both occurrences are legitimate research sentences — reworded
to avoid the pattern while preserving meaning.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
m3taversal 2026-04-07 16:04:14 +01:00
parent 1de60685be
commit e60c0fffb3
2 changed files with 2 additions and 2 deletions
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domains/ai-alignment/LLM-maintained knowledge bases that compile rather than retrieve represent a paradigm shift from RAG to persistent synthesis because the wiki is a compounding artifact not a query cache.md
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@ -33,7 +33,7 @@ Compilation treats knowledge as a maintenance problem — each new source trigge
The Teleo collective's knowledge base is a production implementation of this pattern, predating Karpathy's articulation by months. The architecture matches almost exactly: raw sources (inbox/archive/) → LLM-compiled claims with wiki links and frontmatter → schema (CLAUDE.md, schemas/). The key difference: Teleo distributes the compilation across 6 specialized agents with domain boundaries, while Karpathy's version assumes a single LLM maintainer.
The 47K-like, 14.5M-view reception suggests the pattern is reaching mainstream AI practitioner awareness. The shift from "how do I build a better RAG pipeline?" to "how do I build a better wiki maintainer?" has significant implications for knowledge management tooling.
The 47K-like, 14.5M-view reception suggests the pattern is reaching mainstream AI practitioner awareness. The shift from "building a better RAG pipeline" to "building a better wiki maintainer" has significant implications for knowledge management tooling.
## Challenges

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domains/grand-strategy/attractor-epistemic-collapse.md
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@ -28,7 +28,7 @@ The manuscript's analysis of fragility from efficiency applies directly. Just as
1. **Attention optimization selects for emotional resonance over accuracy** — platforms that maximize engagement systematically amplify content that triggers strong reactions, regardless of truth value
2. **AI collapses production costs asymmetrically** — producing misinformation is now nearly free while verification remains expensive. This is the epistemic equivalent of the manuscript's observation that efficiency gains create fragility
3. **Trust erosion compounds** — as people encounter more synthetic content, trust in all information declines, including accurate information. This is a self-reinforcing cycle: less trust → less engagement with quality information → less investment in quality information → less quality information → less trust
4. **Institutional credibility erodes from both sides** — AI enables both more sophisticated propaganda AND more tools to detect propaganda, but the detection tools are always one step behind, and their existence further erodes trust ("how do I know THIS fact-check isn't AI-generated?")
4. **Institutional credibility erodes from both sides** — AI enables both more sophisticated propaganda AND more tools to detect propaganda, but the detection tools are always one step behind, and their existence further erodes trust ("what guarantees THIS fact-check isn't AI-generated?")
## Evidence it's forming